Abstract
Image quality assessment is of fundamental importance for various image processing applications. A novel method is presented in which the joint occurrences of statistical local representation by log-Gabor filters and texture analysis by local tetra patterns and histograms of colour are considered as quality-aware features. Then the dissimilarities of these features between the distorted and reference images are quantified and mapped into quality score prediction by utilising a support vector regression. Extensive experiments on LIVE, CSIQ and TID databases show that the proposed method is remarkably consistent with human perception and outperforms many state-of-the-art methods, and also it is robust across different distortion types and different databases.
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